Convection Initiation (PGE18 V1.0) v2016

Goal of the CI product

The CI (Convection Initiation) product has been developed by Meteo-France in the framework of the EUMETSAT SAF in support to Nowcasting. Using mainly geostationnary satellite data, it provides the probability for a cloudy pixel to become a thunderstorm in a given following period range. The product aims to catch the first steps of initiation of convection, when the first convective signs occur after the formation of clouds, or when those signs appear revealing a modification of environmental conditions.

Probability of the formation of a thunderstorm depends on evolution of local condition and on advection of clouds. For this second point, CI is unfortunately too scarce for a full object-approach that allows a good following of meteorological systems. CI is a pixel product

CI Algorithm

The process follows :

The detection of cloud systems

The tracking of cloud systems

The discrimination of convective cloud objects

The advection of convective cloud objetcs

Note: relevant parameters, thresholds and some part of the algorithm are inspired from « Best Practice Document, 2013, for EUMETSAT Convection Working Group, Eds J.Mecikalski, K. Bedka and M. König », especially SATCAST methodology for the definition of pre-CI pixels.

Areas of interest

This preliminary step requires in optimum configuration

A first filter is based on 10.8µm BT, to ignore cold cloudy pixels and focus on early warm stages of cloudy pixels

Then, NWP data are used for a guidance when available, to eliminate stable areas and focus on more unstable pixels

NWCSAF cloud type product is used to eliminate non cloudy areas and focus on cloudy pixels

Cloud Type as optional input, is used to ignore cloud-free areas in the identification of cloud cells. It allows CI to focus on cloudy areas only.

CI takes into account the deformation of pixels when on the edge of the spaceview domain. A mask of stretched pixels (pixels more than 5 * nominal_area) is automatically elaborated and backuped, and masked zones are ignored.

Both approaches are merged to focus on areas of interest

CT product

Non cloudy pixels identification

mask for stretched pixels (backup file)

stretched pixels + non cloudy pixels

IR10.8 image

Working image for both object and pixel approach for CI

Thus, large areas are ignored in the following processes, which may focus on a restricted set of pixels to be analyzed.

2D Movement field

A 2D movement field is estimated in optimum configuration with blending NWP wind field in the low level (850hPa) and last available HRW wind observations, remapped on grid-field and selected versus the corresponding pixel’s brightness temperature. Priority in the blending is given to HRW wind observations, affected to a 9-pixels-box centred on the corresponding pixel.

This blended field is at first used as guess movement for initialization in the tracking process of object analysis (“cold start” cases, or orphan cells). Then this field is updated with objects movement vectors, to finally be considered as a pixel tracker for trends calculations.

Object image analysis: warm cloud cell detection&tracking

An object analysis process is undertaken like in RDT-CW software, but has been adapted to focus on warm cloud cells from lowest layers. The objectives of this step are

To take benefit from techniques allowing to catch cloud cells movement

To access cloud cells’ parameters variations along its trajectory

More details about tracking can be found in RDT-CW algorithm description.

CI-specificities rely on

Specific Cold limit for adaptative thresholding : the limit has been set to -25°C (instead of -75°C for RDT-CW) in order to limit the analysis to lowest levels

Minimum vertical extension of objects, which has been set to 3° (instead of 6° for RDT-CW) to focus on lower extended cloud systems

This step takes benefit from movement guess field as input to increase cell’s speed reliability, and on the other hand delivers as output an updated movement field with the analyzed objects’ speeds. All pixels belonging to a tracked cloud system are affected the corresponding movement speed instead of previous pixel’s movement values.

This final blended movement field is a key point for further relevant trends calculations

Pixel image analysis

Brightness Temperature Differences are processed for each eligible-CI pixel from various available channels, for current data and data from previous slot.

BTDs taken into account are

WV6.2-WV7.3,

WV6.2-IR10.8

IR10.8-IR8.7

IR12.0-IR10.8,

IR13.4-IR10.8

Then, BT (IR10.8) and BTD trends are calculated for each eligible-CI pixel using the speed and direction of updated 2D movement field as guidance for identifying pairs of current and corresponding pixels in previous image.

When tracking of aggregated pixels (belonging to a tracked cloud system as object) is available, corresponding trends are used for some parameters instead of single pixel-trends, and should be able to provide trends over longer depth.

Interest fields

Each eligible-CI has then a list of BT and BTD values and trends. According to previous studies about convection initiation, parameters are grouped as:

CI-diagnosis parameters are a subset of Pre-CI parameters : In the last step, only some parameters will be analyzed for a CI-diagnosis.

CI diagnosis

CI-diagnosis should be derived from statistical models using Interest fields’ values of pre-CI pixels. Those models will rely on a specific ground truth (high reflectivity from radar data, convective cells from RDT-CW, lightning data). In logistic regression a pixel that belongs to a convective path during a given period will be considered as ground truth. Such a tuning has been postponed to the next release.

Current CI output is estimated with empirical rules defined by count of relevant criteria. The v2016 version proposes this possibility only. The principle is to sum up the number of relevant parameters (i.e. above relevant threshold given in the table above) by group, giving greater importance to growth family parameters, then glaciation parameters, and finally vertical extension (height) group.

Nb of Growth relevant parameters (over 3)

Nb of Glaciation relevant parameters (over 3)

Nb of Height relevant parameters (over 4)

Result

>or= 2

>or= 3

>or= 4

HIGHPROB

>or= 3

MODPROB

< 3

LOWPROB

>or= 2

>or= 4

MODPROB

>or= 3

LOWPROB

<3

VLOWPROB

>or= 1

>or= 3

>or= 4

MODPROB

< 4

LOWPROB

>or= 2

>or= 4

LOWPROB

>or= 3

VLOWPROB

0

>or= 3

>or= 3

LOWPROB

<3

VLOWPROB

Other cases

0

Empirical rules for CI-diagnosis. HIGHPROB means between 75 and 100%, MODPROB between 50 and 75%, LOWPROB between 25 and 50%, VLOWPROB between 0 and 25%

The output products

Data are provided in a NetCDF format. The final product concerning CI includes probability of Convective Initiation for 30min, 60min and 90min ranges (resp ci_prob30, ci_prob60 and ci_prob90 Netcdf container). But for this v2016 release, only the first range (ci_prob30) is initialized, other ranges present undefined data. Data are encoded regarding the correspondance table below.

code

ci_prob30 container

0

no probability to become thunderstorm

1

0-25% probability to become thunderstorm in the next 30minutes

2

25-50% probability to become thunderstorm in the next 30minutes

3

50-75% probability to become thunderstorm in the next 30minutes

4

75-100% probability to become thunderstorm in the next 30minutes

Example of CI probability for next 30min. Here a value of 3 stands for a probability of 75%

The Current CI-Netcdf format is described in detail in the Document Output Format (DOF) of SAFNWC.

The configuration file

Input Data

CT 0(default) or 1 if using CT product for additional attributes Cloud Type and Cloud PhaseCMa 0(default) or 1 if using CT product for masking non cloud pixelsHRW 0(default) or 1 if using HRW product as guess data for movement estimation